summaryData<-read.csv("figure5.csv",header=T,as.is=T)

#################
# Figure 5
#################

p <- ggplot(summaryData, aes(x=names, y=modelcoef, ymin=ylo, ymax=yhi)) + 
	geom_pointrange(colour=ifelse(summaryData$ylo < 0 & summaryData$yhi > 0, "red", "blue")) + 
	theme_pew()  + 
	geom_hline(aes(yintercept=0), lty=2) + 
	coord_flip() + 
	ylab("Percent of speeches referencing bipartisanship\n(95% CIs, Clustered Robust Standard Errors,\nCongress Fixed Effects)") + 
	xlab("") #+ ggtitle("Estimated Change in Proportion of Floor Speeches with Bipartisan Rhetoric") 
p
ggsave("f5.eps",p, width=6, height=2)

#################
# Figure 6
#################
data<-read.csv("figure6.csv",header=T,as.is=T)

p <- ggplot(data, aes(x=names, y=modelcoef, ymin=ylo, ymax=yhi)) + 
	geom_pointrange(colour=ifelse(data$ylo < 0 & data$yhi > 0, "red", "blue")) + 
	theme_pew()  + 
	geom_hline(aes(yintercept=0), lty=2) + 
	coord_flip() + 
	ylab("Percent of speeches referencing bipartisanship\n(95% CIs, Clustered Robust Standard Errors,\nCongress Fixed Effects)") + 
	xlab("") #+ ggtitle("Estimated Change in Proportion of Floor Speeches with Bipartisan Rhetoric") 
p
ggsave("f6.eps",p, width=6, height=2)
